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---
dataset_info:
features:
- name: imageA
dtype: image
- name: imageB
dtype: image
- name: label
dtype: image
splits:
- name: train
num_bytes: 26938135.0
num_examples: 827
- name: test
num_bytes: 12564707.0
num_examples: 385
download_size: 39371405
dataset_size: 39502842.0
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
---
# Onera Satellite Change Detection Dataset - RGB - Cropped to 96x96 patches
<!-- Provide a quick summary of the dataset. -->
The Onera Satellite Change Detection dataset addresses the issue of detecting changes between satellite images from different dates.
It comprises 24 pairs of multispectral images taken from the Sentinel-2 satellites between 2015 and 2018. Locations are picked all over the world, in Brazil, USA, Europe, Middle-East and Asia. For each location, registered pairs of 13-band multispectral satellite images obtained by the Sentinel-2 satellites are provided. Images vary in spatial resolution between 10m, 20m and 60m.
Pixel-level change ground truth is provided for all 14 training and 10 test image pairs. The annotated changes focus on urban changes, such as new buildings or new roads. These data can be used for training and setting parameters of change detection algorithms.
## Dataset Details
### Dataset Description
The images were cropped to 96x96 patches to enable direct usage with the standard protocol.
- **License:** CC BY-NC-SA
### Dataset Sources
<!-- Provide the basic links for the dataset. -->
- **Homepage:** [https://rcdaudt.github.io/oscd/](https://rcdaudt.github.io/oscd/)
- **Paper:** [ArXiv](https://arxiv.org/abs/1810.08468)
## Uses
<!-- Address questions around how the dataset is intended to be used. -->
```python
from datasets import load_dataset
oscd96 = load_dataset("blaz-r/OSCD_RGB_Cropped_96")
>>> oscd96["train"]
# single "sample" with 3 features: "imageA", "imageB", "label"
```
### Recommended protocol
If you use this dataset, we encourage you to also consider following the evaluation protocol disscused in paper:
**Be the Change You Want to See: Revisiting Remote Sensing Change Detection Practices**
[Paper (arXiv)](https://arxiv.org/abs/2507.03367)
[Paper (TGRS)](https://doi.org/10.1109/TGRS.2025.3585342)
This protocol (Sec III-B) standardizes evaluation across change detection benchmarks and helps ensure fair and reproducible comparisons.
## Citation
If you use this work for your projects, please take the time to cite the original paper:
```bibtex
@inproceedings{daudt2018urban,
title={Urban change detection for multispectral earth observation using convolutional neural networks},
author={Daudt, Rodrigo Caye and Le Saux, Bertr and Boulch, Alexandre and Gousseau, Yann},
booktitle={IGARSS 2018-2018 IEEE International Geoscience and Remote Sensing Symposium},
pages={2115--2118},
year={2018},
organization={IEEE}
}
``` |